An interpretable CT-based deep learning model for predicting overall survival in patients with bladder cancer: a multicenter study.
Authors
Affiliations (14)
Affiliations (14)
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
- Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
- Department of Colorectal Surgery, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
- Medical Imaging, The Medical College of Qingdao University, Qingdao, China.
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China.
- Department of Radiology, The Puyang Oilfield General Hospital, Puyang, China.
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
- Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China. [email protected].
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China. [email protected].
- Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China. [email protected].
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China. [email protected].
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China. [email protected].
Abstract
Predicting the prognosis of bladder cancer remains challenging despite standard treatments. We developed an interpretable bladder cancer deep learning (BCDL) model using preoperative CT scans to predict overall survival. The model was trained on a cohort (n = 765) and validated in three independent cohorts (n = 438; n = 181; n = 72). The BCDL model outperformed other models in survival risk prediction, with the SHapley Additive exPlanation method identifying pixel-level features contributing to predictions. Patients were stratified into high- and low-risk groups using deep learning score cutoff. Adjuvant therapy significantly improved overall survival in high-risk patients (p = 0.028) and women in the low-risk group (p = 0.046). RNA sequencing analysis revealed differential gene expression and pathway enrichment between risk groups, with high-risk patients exhibiting an immunosuppressive microenvironment and altered microbial composition. Our BCDL model accurately predicts survival risk and supports personalized treatment strategies for improved clinical decision-making.